1 of maasai pastoralist in kenya to climate change and

Article 1

Assessing Vulnerability of Maasai Pastoralist in 2

Kenya to Climate Change and Variability 3

Ayodotun Bobadoye *, William Ogara, Gilbert Ouma and Joshua Onono 4

1 Institute for climate change and adaptation, University of Nairobi, Nairobi, Kenya; [email protected] 5 (W.O.); [email protected] (G.O.); [email protected] (J.O.) 6

* Correspondence: [email protected]; 7

Abstract: Human adaptive responses to climate change occur at the local level, where climatic 8 variability is experienced. Therefore analyzing vulnerability at the local level is important in 9 planning effective adaptation options in a semi‐arid environment. This study was conducted to 10 assess vulnerability of Maasai pastoralist communities in Kajiado County, Kenya to climate change 11 by generating vulnerability index for the communities. Data was collected using questionnaires that 12 were administered to 305 households in the five different administrative wards 13 (Oloosirkon/Sholinke, Kitengela, Kapetui North, Kenyawa‐Poka and Ilmaroro) in Kajiado East. 14 Vulnerability was measured as the net effect of adaptive capacity, sensitivity and exposure to 15 climate change. Principal Component Analysis (PCA) was used to assign weights to the 16 vulnerability indicators used for the study and also to calculate the household vulnerability index. 17 A vulnerability map was produced using the GIS software package ArcGIS 10.2. Results showed 18 that gender of household head, age of household head, educational level, access to extension agents, 19 herd size, livestock diversity and access to credit facility influenced vulnerability of the Maasai 20 pastoralists to climate change in Kajiado East. The result showed that the most vulnerable 21 communities with the highest negative vulnerability index value are Ilpolosat (‐2.31), Oloosirikon 22 (‐2.22), Lenihani (‐2.05), Konza (‐1.81) and Oloshaiki (‐1.53). The communities with the highest 23 positive vulnerability index values were Kekayaya (4.02), Kepiro (3.47), Omoyi (2.81), Esilanke 24 (2.23), Kisaju (2.16) and Olmerui (2.15). We conclude that provision of basic amenities such as good 25 roads and electricity; access to extension agents, access to credit facilities and herd mobility will 26 reduce vulnerability of Maasai pastoralists in Kajiado east to climate change and variability 27

Keywords: vulnerability index, Maasai pastoralists, principal component analysis, climate change 28 29

1. Introduction 30

Many studies have been conducted on vulnerability to climate change and its extremes and 31 different researchers have defined vulnerability according to their own perception. Adger (1999) 32 defined vulnerability to climate change as “the extent to which a natural or social system is 33 susceptible to sustaining the damage from climate change”. IPCC (2014) defines vulnerability to 34 climate change as “the degree of system susceptibility and its inability to cope with adverse effect of 35 climate change and variability. Therefore vulnerability is a function of character, magnitude and rate 36 of climate change and variability to which a system is exposed to. This also includes its sensitivity 37 and adaptive capacity to climate change and variability”. 38

The concept and definition of vulnerability that has been used by different studies revolves 39 around the explanation of lack of adaptive capacity in both social and natural system. Climate 40 change vulnerability has been studied by different scholars as a composite of adaptive capacity, 41 sensitivity and exposure to hazard (Adger and Kelly 1999; Paavola 2008; Yuga et al., 2010; Deressa, 42 2010; Acheampong et al., 2014). Adaptive capacity can be defined as the ability to withstand or adjust 43 to the changing context; it is the ability to implement adaptation measures that help avert potential 44 impacts of climate change and variability (Opiyo, 2014; Acheampong et al., 2014). Sensitivity can be 45 defined as the ability of a system to be affected by climate change and its extremes; it describes 46

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conditions that can trigger an impact or ameliorate hazard. Exposure is the nature and change in 47 climate variables and extreme events; it is the physical impact of climate change such as change in 48 rainfall pattern or rise in temperature range (Kasperson et al., 1995; Paavola 2008; Opiyo, 2014). 49

Climate change vulnerability can be analyzed from global level (IPCC 2014; Brooks 2004) to 50 regional level (Deressa et al., 2009; Acheampong et al., 2014) and household level (Opiyo, 2014). The 51 choice of vulnerability analysis scale depends on the aim of the research, available data and the 52 methodology of the study. Most of the available scientific literatures on climate vulnerability analysis 53 focus on national and regional vulnerability assessment usually for national or regional adaptation 54 planning (Opiyo et al., 2014; Hinkel 2011). While vulnerability analysis at the national level is 55 necessary for policy formulation and national planning; household vulnerability assessment 56 conceptualizes how climate change and variability impacts directly on the household members and 57 measures their ability to adapt. This is particularly useful for resource allocation and planning for 58 adaptation strategies at the local level. Pearson et al., (2008) and Sherwood (2013) reported that 59 vulnerability indices are diverse for the different multiple spatial scales and that household 60 vulnerability assessment can be used to demonstrate how climate change affects livelihood of 61 different communities. The aim of this study is to measure vulnerability of Maasai pastoralists’ 62 communities to climate change and to develop vulnerability maps showing the levels of vulnerability 63 of Maasai pastoralist households to climate change and variability. This study will assist policy 64 makers in resource allocation and climate adaptation planning in the arid and semi‐arid lands of 65 Kenya. 66

2. Methodology 67

2.1. Study area 68

The study was conducted in Kajiado East Sub‐County (Figure 1). Kajiado East has a high 69 population of Maasai tribe and Pastoralism is the main source of livelihood to a majority of 70 households. The livestock breeds kept include sheep, goat, beef and dairy cattle and donkey. About 71 90% of the area is categorized as semi‐arid eco‐climatic zones and rainfall pattern is bimodal. The 72 long rains season starts in March and this peaks in April and continues till May. The short rains begin 73 in October and ends in December (Amwata, 2013; Bobadoye et al., 2014). 74

2.2. Data collection 75

Data were collected using semi‐structured questionnaires administered to household heads in 76 the five administrative wards in Kajiado East (Oloosirkon/Sholinke, Kitengela, Kapetui North, 77 Kenyawa‐Poka and Ilmaroro). A total of 305 household questionnaires were administered and 20 key 78 informant interviews conducted between October 2014 and January 2015. Sample size was 79 determined according to Krejcie and Morgan (1970) method of determining a sample size. Estimation 80 of sample size in research using Krejcie and Morgan used the formula below to determine sample 81 size: 82

S = X2NP (1‐P) ÷ d2 (N‐1) + X2P (1‐P) 83 Where: 84 S = required sample size 85 X2 = the table value of chi‐square for 1 degree of freedom at the desired confidence level (95%) 86

(3.841) 87 N = the population size 88 89 P= the population proportion (assumed to be .50 since this would provide the maximum sample 90

size) 91 d = the degree of accuracy expressed as a proportion (.05). 92 Based on 1800 Maasai pastoralist households in Kajiado east, a total of 56 villages and 305 Maasai 93

pastoralist household were sampled in this study 94



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The questionnaire used for the study was divided into the following: Household demographics, 95 socio‐economic characteristics, source of family income, livestock and crop production, basic 96 amenities owned, size of land owned, access to extension service, access to credit facilities, perception 97 to climate change, adaptation strategies, access to weather information and other relevant 98 information. 99

100

Figure 1: Map of the study area 101

2.3. Vulnerability Analysis 102

This study analyzed vulnerability of Maasai pastoralist as a net effect of adaptive capacity, 103 exposure and sensitivity. 104

Vulnerability = Adaptive capacity – (exposure + sensitivity) ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ (1) 105 The integrated vulnerability assessment method was used to analyze vulnerability of Maasai 106

pastoralist to climate change. Vulnerability is a function of the character, magnitude and rate of 107 climate variation to which a system is exposed, its sensitivity and its adaptive capacity”. When 108 adaptive capacity of the pastoralist household is less than the sensitivity and exposure, the household 109 becomes more vulnerable to climate change impacts and the reverse is also true, the higher the 110 adaptive capacity, the less vulnerable the household to climate change impact. This method uses a 111 combination of indicators to measure vulnerability by computing indices and weighted average for 112 the selected indicators. The indicators used in this study were selected based on researchers’ 113 observation, literature review of published research done in pastoralists’ communities and the 114 opinion of the Maasai pastoralist’s communities in Kajiado County. Community involvement is 115 important in selecting indicators for vulnerability analysis. This is because vulnerability to climate 116 change is location specific. 117

The principal component analysis (PCA) was used to generate factor scores for calculating the 118 vulnerability index for the households. In this study, the first principal component is the linear index 119 of all the variables that captures the highest amount of information common to all variables. 120

The vulnerability index was determined based on three vulnerability components (adaptive 121 capacity, exposure and sensitivity. Vulnerability index of household was calculated using the 122 equation below: 123

Vi = (A1X1J + A2X2J + ……… + AnXnj) – (A1Y1j + A2Y2j + ……….. AnYnj) ‐‐‐‐‐‐‐‐‐‐‐‐ (2) 124



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125 Where Vi = vulnerability index 126 Xs = indicators for adaptive capacity 127 Ys = indicators for exposure and sensitivity 128 As = First component score of each variable computed using PCA. 129 The values of X and Y were obtained by normalizing the values of vulnerability indicators using 130

their mean and standard deviations. In this study, Vulnerability index was calculated using 28 131 vulnerability indicators selected for adaptive capacity, exposure and sensitivity. Vulnerability index 132 were generated for the 305 pastoralist household interviewed in 56 communities in Kajiado East 133 (Table 1). 134

Table 1: Distribution of sampled villages in each ward 135

Wards Villages sampled and number of questionnaires sampled per

village

Total number of household

sampled per ward

Kaputie North

Emampariswai (4), Enkileele (3), Enkirgirri (3), Ilkiushin (3), Ilpolosat

(5), Isinya (8), Kekayaya (5), Kisaju, (8), Lenihani (3), Noosuyian (3),

Ntipilikuani (3), Olepolos (3), Olkinos (3), Olmerui (5), Oloshaiki (2),

Olturoto, (6), Ormoyi, (4).

69

Kitengela

Embakasi (8), Enkasiti (8), Kepiro (6), Kitengela (8), Korrompoi (7),

Mbuni (7), Nado Enterit (5), Naserian (8), Nkukuon (5),

Olooloitikoshi (5)

67

Sholinke Embakasi (6), Enkutoto o mbaa (9), Kware (8), Nkukuon (6),

Olooloitikoshi (9), Oloosirikon (8), Sholinke (6) 56

Kenyawa Poka Arroi (8), Esilanke (5), Kenyawa (6), Kibini (4), Mashuuru (5),

Noompaai, (6), Olgulului (5), Oltepesi (8), Poka (4), Sultan (5) 55

Imaroro

Arroi (4), Imaroro (4), Konza (6), Mbilin (6), Oibor Ajijik (4),

Olekaitoriori (10), Olgulului (8), Oloibor Ajijik (7), Oltepesi (7), Wulu

(8).

57`

The average vulnerability index for each community was determined by calculating the mean 136 vulnerability index for the communities. The study presented the level of vulnerability of households 137 and Maasai communities in the study area in a map. The vulnerability maps showing the levels of 138 vulnerability of Maasai pastoralist communities (highly vulnerable, moderately vulnerable and less 139 vulnerable) in the five administrative wards in the study area was produced using Geographical 140 Information System (GIS) software package ArcGIS 10.2. Focus group discussions and key informant 141 interviews with Maasai pastoralist and stakeholder meetings were conducted in the study area to 142 verify and validate the vulnerability maps produced in this study. 143

The level of influence of the indicators on vulnerability of the households was also analyzed 144 using the ordinal logistic regression model. The model is used when results are presented in ordinal 145 scales, as in this study where vulnerability is categorized into (1) highly vulnerable (2) moderately 146 vulnerable and (3) less vulnerable households. 147

The reduced form of ordinal logistic regression used in this study as described by Green (1997) 148 is given as: 149

∗ = + Uij ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ (3) 150

Where Y = Level of vulnerability and involves ordered vulnerability categories, Y = 1 was given 151 to highly vulnerable households, Y= 2 was given to moderately vulnerable households and Y= 3 was 152 given to household less vulnerable households. Y* is the given state of vulnerability. The Xij are the 153 explanatory variables determining vulnerability level. βs are parameters estimated and Uij is the 154 disturbance term. 155 156



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3. Results and Discussion 157

3.1. Vulnerability indicators and expected direction with respect to vulnerability 158

The vulnerability indicators used for this study are presented in table 1. The indicators were 159 selected jointly by the researcher and the Maasai communities. These vulnerability indicators were 160 categorized according to the definition of vulnerability as a function of adaptive capacity, exposure 161 and sensitivity. In this study, the adaptive capacity is represented by wealth, infrastructure, access to 162 information, literacy level and household size and the number of dependents. Wealth enhances the 163 ability of communities to cope and recover from climate extremes. Size of herds, size of land owned 164 and mobility of livestock are indicators used by Maasai pastoralist to assess the level of wealth of 165 pastoralist households (Opiyo et al., 2014). O’ Brien et al. (2004) reported that availability of basic 166 infrastructures plays an important role in adaptation to climate change. It increases the ability of rural 167 dwellers to diversify their sources of income thereby enhancing their adaptive capacity. Likewise, 168 availability of hospitals can enhance the provision of preventive treatments for diseases associated 169 with climate change such as malaria and meningitis. 170

In this study, sensitivity is represented by level of education, household size, gender and age of 171 household head. It is believed that the level of education of the household head impact on the 172 sensitivity of the household to climate variability and change. It has also been reported that 173 households with smaller size are more likely to withstand climate change and its extreme (Opiyo, 174 2014). Exposure in this study is represented by the frequency of extreme climatic events such as 175 droughts and floods and also by change in temperature and precipitation amount. 176

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Table 1: Vulnerability indicators and expected direction with respect to vulnerability 178

Determinants of

Vulnerability

Vulnerability

indicators

Description of indicator

used for analysis

Relationship between indicator and

vulnerability

Adaptive capacity Wealth Herd size, livestock

diversity, land size, non‐

farm income, crop farming

income

The more the size of land own and

income generated by households the

less the vulnerability to climate

change

Access to information Visit by extension agents,

access to climate

information

The more access the household has

to climate information the less their

vulnerability

Infrastructures and

asset

Access to electricity, toilet

and hospitals. Own radio

and TV

The more the households that have

access to electricity, hospitals and

other asset the less their vulnerability

Sensitivity Household

characteristics

Household size, number of

dependent, marital status,

gender of household head,

age of household head

The higher household size and

number of dependent, the higher the

vulnerability. Female headed

households are more vulnerable

Literacy level Level of education The higher the literacy rate, the less

the vulnerability

Exposure Extreme climates Frequency of drought and

floods

The higher the frequency of extreme

events the more the vulnerability

Change in climate Temperature change

Precipitation change

Reduced rainfall and increase

temperature increase vulnerability

179



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Table 2. show that only 8.5% of the households were headed by females and these confirms the 180 findings of Omolo (2010) that Maasai communities are patriarchal and women are less involved in 181 decision making, and are often relegated to taking care of the children and other household activities. 182 This is expected to reduce the female headed household early access to climatic information and early 183 warning information and affect their ability to respond early to extreme climatic events. Data on 184 household size shows that 91.5% of respondents had household size of more than Five (5) people. 185 Results showed that 33% of the household heads had no formal education and level of education 186 affect the ability of pastoralist to adapt to climate change. Lack of formal education affects the ability 187 of the household to understand and interpret climate information for decision making (Opiyo et al., 188 2014). Various findings (Yohe and Tol 2002; Skjeflo 2013; Opiyo 2014) has shown that household size 189 has a significant influence on the vulnerability of the households to climate change and climate 190 extremes. 191

Smaller households are usually less susceptible to climate extreme events such as drought. This 192 is because food scarcity is one of the main challenges during drought and the lesser the household 193 size, the easier it is to cope with scarcity of food. The results also showed that other adaptive capacity 194 indiators such as marital status, access to extension agents, herd size, livestock diversity and access 195 to credit facilities positively influenced vulnerability in the study area. This result concur with the 196 findings of Katoka et al., (2011) and Opiyo et al., (2014) which similarly reported that most of these 197 variables affects household vulnerability to climate change in the pastoralist communities. 198

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Table 2: Indicators and their effects on vulnerability 200

Hypothesized variables Percentage of household Influence on vulnerability

Sensitivity Indicators

Gender of HH head: female headed households 8.5 +

Age of HH head: 50+ years 64.9 +

Experience in the area: 45+ years 56.4 ‐

HH size: 5+ persons 91.5 +

Education level: no primary education 33.0 +

Dependents: 5+ persons 9.6 ‐

Marital status: single (including divorced and widowed) 10.6 +

Visit by extension officers: no access to extension services 83.0 +

Receive climate information 90.0 ‐

Adaptive capacity Indicators

Crop‐farming income: with income from crop farming 7.4 ‐

Non‐farm income: with income from non‐farm activities 74.5 ‐

Herd size: 100+ total herd size 85.1 ‐

Livestock diversity: own 2+ domestic animal types 93.4 ‐

Land size: own 100+ acres 57.4 ‐

HH members employed: 3+ members employed 36.2 ‐

Credit access: have no access to credit 72.3 +

Livestock mobility: able to move livestock freely 62.8 ‐

Own radio 94% ‐

Own TV 68% ‐

Access to electricity 22% ‐

Access to hospital 94% ‐

Access to toilet 72% ‐

Exposure Indicators

Temperature: noticed increase 93.6 +

Rainfall: noticed decrease 90.0 +

Drought: experience drought within the last 10 years 100.0 +

Floods: experience floods within last the 10 years 74.5 +

Drought frequency: every year 47.9 +

Floods frequency: every year 2.1 ‐

Positive sign means indiators increase vulnerability while negative sign means they 201

3.2. Vulnerability Analysis of Maasai pastoralist to climate change in Kajiado County 202

Table 3 shows the result of the factor score for the first principal component analysis and its 203 association with the vulnerability variables. Principal Component Analysis was run on the indicators 204 listed in Table 1 to generate the factor scores. The first principal component was used to generate the 205 factor scores (weight) because it explains 91% of the variations. Vulnerability index was computed 206 based on the definition of vulnerability in equation (2) which defines vulnerability as a net effect of 207 adaptive capacity minus exposure and sensitivity. The indicators of adaptive capacity which were 208 positively associated with the first principal component analysis and the indicator of sensitivity and 209 exposure, which were negatives associated with the first principal component analysis were used to 210 calculate the vulnerability index in the study. This is because the vulnerability equations shows that 211 increase in adaptive capacity contributes to reduction in vulnerability, while increase in exposure and 212 sensitivity increases vulnerability. The variables with higher factor scores have higher influence on 213 vulnerability in the study area. The vulnerability of households in the study area was classified 214 based on the different communities in the study area using the vulnerability index. 215

The result of vulnerability index of communities in Kajiado east is presented in table 4. This 216 study calculated vulnerability index for 305 households in 56 Maasai communities in the five 217 administrative wards in Kajiado east sub‐County. The vulnerability index of the communities was 218 determined by calculating the average vulnerability index for households in each community. The 219



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results shows that Ilpolosat, Oloosirikon, Lenihani, Konza and Oloshaiki were the most vulnerable 220 communities having the highest negative vulnerability index value of ‐2.31, ‐2.22, 02.05, ‐1.81 and ‐221 1.53 respectively. The least vulnerable communities with the highest positive vulnerability index 222 values were Kekayaya, Kepiro, Omoyi, Esilanke, Kisaju and Olmerui with values of 4.02, 3.47, 2.81, 223 2.23, 2.16 and 2.15 respectively. The vulnerability index of communities varied between 4.02 to ‐2.31. 224 The result shows high disparity in the vulnerability of communities in Kajido east. It concur with the 225 findings of Orindi et al., (2007) which reported that land sub division and sales among the Maasai in 226 Kajiado has increased the standard of living of few Maasai while most are left highly vulnerable and 227 unable to practices their pastoralist system. Increase in dry spell and drought over the last few 228 decades coupled with restriction in animal movement have also increased vulnerability of Maasai 229 pastoralist to climate change and variability ( Kakota et al., 2011; Opiyo et al., 2014). 230

Table 3: Factor scores for the first principal component analysis 231

Factors Factor

Scores Social vulnerability variables

Gender House Hold head 0.02

Age of HH head: 50+ years ‐0.0138

Experience in the area: 45+ years 0.0158

HH size: 5+ persons ‐0.051

Education level: no primary education ‐0.13

Visit by extension workers: no access to extension services ‐0.01

Receive climate information 0.001

Dependents: 5+ persons ‐0.06

Marital status of HH head: single (including divorced and widowed) 0.04

Own radio 0.0000

Own television 0.4

Own mobile phone 0.3

Access to electricity 0.2

Toilet 0.19

Access to a hospital 0.003

Economic vulnerability variables

Crop farming income: with income from crop farming 0.19

Non‐farm income: with income from non‐farming activities 0.04

Herd size: 100+ total herd size 0.286

Livestock diversity: own 2+ domestic animal types 0.22

HH members employed: 3+ members employed 0.030

Credit access: have no credit access 0.002

Livestock mobility: able to move livestock freely 0.15

Land size: own 100+ acres 0.90

Environmental vulnerability variables

Rainfall: noticed decrease ‐0.02

Temperature: noticed increase ‐0.12

Drought: experienced drought within the last 10 years ‐0.22

Floods: experienced floods within the last 10 years 0.0001

Drought frequency: every year ‐0.11

Floods frequency: every year 0.0004

232

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Table 4: Vulnerability index of Maasai communities in Kajiado east sub‐County 234

Villages Vulnerabilit

y index

X Coordinate Y Coordinate Villages Vulnerability

index

X Coordinate Y Coordinate

Kisaju 2.16 36.82 ‐1.6 Wulu 0.87 37.14 ‐1.81

Isinya ‐0.37 36.84 ‐1.69 Esilanke 2.23 36.76 ‐1.72

Olmerui 2.15 36.88 ‐1.75 Olgulului 1.02 37.30 ‐2.23

Poka ‐0.31 37.45 ‐2.13 Noompai ‐1.15 37.46 ‐2.34

Sultan ‐0.21 37.37 ‐2.02 Kekayaya 4.02 37.46 ‐1.59

Kitengela 0.36 36.96 ‐1.47 Lenihani ‐2.05 37.06 ‐1.69

Konza ‐1.81 37.13 ‐1.74 Embakasi 0.057 36.83 ‐1.39

Mashuru 0.73 37.13 ‐2.10 Kware ‐0.11 36.81 ‐1.46

Oletepes 0 36.77 ‐1.47 Enkutoto mbaa 0.02 36.74 ‐1.43

Olooloitikoshi 0.48 36.80 ‐1.57 Kepiro 3.47 36.85 ‐1.64

Oloosirikon ‐2.22 36.81 ‐1.43 Korrompoi ‐0.37 36.92 ‐1.62

Sholinke ‐0.19 36.78 ‐1.51 Olturoto ‐1.51 36.90 ‐1.64

Iltepes 0.23 37.23 ‐2.19 Naserian 0.43 36.97 ‐1.66

Kenyawa 1.44 37.58 ‐2.19 Emampariswai 0.49 36.98 ‐1.70

Kibini 0.44 37.29 ‐2.13 Ntipilikuani 0.02 36.67 ‐1.69

Arroi 0.08 37.30 ‐2.02 Olepolos 1.54 36.70 ‐1.51

Imaroro ‐1.33 37.09 ‐1.95 Oloshaiki ‐1.53 36.95 ‐1.56

Mbilini 1.68 37.05 ‐1.97 Ormoyi 2.81 36.73 ‐1.65

llkuishin ‐0.31 36.94 ‐1.75 Enkasiti ‐0.14 36.87 ‐1.57

Enkirigirri 0.30 36.85 ‐1.78 Mbuni ‐0.44 36.87 ‐1.55

Ilpolosat ‐2.31 37.06 ‐1.75 Oloibor Ajijik 0.74 37.06 ‐1.89

Olkinos ‐1.15 36.88 ‐1.66

Olekiatorio 0.31 37.17 ‐1.20

3.3. Vulnerability maps of households and communities in Kajiado East sub County 235

Maps have the advantage of presenting data in an easily assessable, readily visible and eye 236 catching manner. Mapping vulnerabilities to climate change is a key planning tool for government 237 and policy makers in resources allocation and adaptation planning. There is the urgent need in Kenya 238 for availability of information especially at the local levels where intervention are needed for 239 establishing early warning systems, disaster risk response and capacity building. 240



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241

Figure 2: Map showing the level of vulnerability of households in Kajiado East to climate change and 242 variability 243

Figure 2 shows the vulnerability map of households in Kajiado east sub‐County. The map shows 244 that households in most communities in Kajiado east are moderately vulnerable to climate change 245 and variability. The map also revealed a high level of variation in the vulnerability of some 246 households within the same community. This shows that although communities are exposed to the 247 same climatic factors, the adaptive capacity of the household has a significant effect on its 248 vulnerability. Fussel (2007) and Deressa (2010) reported that individual households within the 249 community vary in socio‐economic characteristics such as level of education, wealth, access to credit 250 and political power; which are responsible for variation in vulnerability levels. Mapping household 251 vulnerability is important in identifying vulnerable household within a community and also 252 understanding vulnerability pattern of household in the community. However, mapping 253 vulnerability at the community level provides information for policy makers and decision makers to 254 take informed decisions that will enhance resilience of vulnerable communities. 255

Figure 3 shows the vulnerability map of communities in Kajiado east. The map categorized 256 communities into highly vulnerable, moderately vulnerable and vulnerable communities based on 257 their vulnerability index. The maps revealed that although most communities in Kaputie North ward 258 are moderately vulnerable; Maasai communities in Iloposat, Lenihani, Oloshaiki and Olturoto are 259 highly vulnerable to climate change and variation. Kitengela ward has the highest number of Maasai 260 communities that are less vulnerable; this might be due to the availability of basic amenities such as 261 good roads, electricity and hospitals. Maasai communities that are highly vulnerable to climate 262



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change and variability in Sholinke ward are communities living in Oloosirikon and Korrompoi. The 263 other Maasai communities in Sholinke are moderately vulnerable to climate change and variability. 264 The map shows that communities in Mbilin and Koonza of Imaroro ward are also highly vulnerable 265 to climate change and variability. In Kenyawa‐Poka ward, most communities are moderately 266 vulnerable to climate change and variability, however, the map shows that communities in Noompai 267 are highly vulnerable while those in Esilanke are less vulnerable to climate change and variability. 268

269

Figure 3: Map showing the level of vulnerability of communities in Kajiado East to climate change 270 and variability 271

The maps further revealed that Kaputiei North sub county has the highest number of highly 272 vulnerable households, followed by Oloosirkron/Shilonke, Imaroro and Kitengela has almost the 273 same number of household that are highly vulnerable while Kenyawa‐Poka has the least number of 274 households that are highly vulnerable. The household that are highly vulnerable are unable to cope 275 with the adverse effect of climate change and variability and needs immediate external assistance in 276 terms of relief to survive. Previous studies in ASALs of Kenya (Orindi et al., 2007; Omolo 2010; 277 Amwata, 2013) reported that it is becoming difficult for households to recover from changing and 278



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inconsistent weather conditions affecting the pastoralist livelihood. The result is also consistent with 279 the findings of Opiyo et al., (2014); and Ongora and Ogara (2012) conducted in similar ecosystem in 280 Kenya. 281

Findings from this study shows an urgent need for evidence based policies and plans to improve 282 the adaptive capacity of Maasai pastoralist through provision of basic amenities and also well 283 structured early warning and disaster response systems to reduce their vulnerability to climate 284 change and variability. 285

3.4. Variables influencing household vulnerability in the study area 286

The result of the ordered logistic regression model for the variables influencing the vulnerability 287 of household is presented in table 4. A total of nine variables have significant influence (at 5% and 288 10% levels of significance) on vulnerability to climate change in the study area. The result shows that 289 gender of household head, years of experience in the area, educational level, visit by extension agents, 290 herd size, livestock diversity, land size and livestock mobility has significant influence on 291 vulnerability in the study area. 292

The Maasai communities are typically patriarchal and female headed households, households 293 that access to extension agents and those with low level of education are significantly vulnerable to 294 climate change. This is because such households either lack access to information for early decision 295 making during extreme climatic events or lack the economic capacity to act on decisions during 296 extreme conditions. 297

Table 4: Variables influencing household vulnerability to climate change and variability 298

Variables Estimates SE OR Z P‐value

Gender of HH head: female headed HH 1.9585 0.5522 5.34 3.561 0.02910*

Age of HH head: 50+years 0.02838 0.95623 0.083 0.029 0.76657

Experience in the area: 45+years 0.48827 0.039463 1.409 12.237 0.06598**

HH size: 5persons and above 0.02110 0.369333 0.051 0.057 0.95443

Educational level: no primary education 0.75148 0.650586 2.175 1.269 0.07776**

Dependents:5+persons ‐0.12651 0.137281 0.372 ‐0.922 0.35676

Marital status: Single (including divorced and widowed ‐0.811681 0.951857 2.312 ‐0.853 0.09381**

Visit by extension officers: no access to extension services ‐1.50143 0.475808 4.501 ‐1.217 0.0898**

Receive climate information 1.0987 0.876 4.439 1.6323 0.6721

Crop‐farming income: with income from crop farming 0.803874 1.540628 2.804 0.522 0.60182

Non‐farm income: with income from non‐farm activities 0.14548 1.303264 0.335 0.112 0.91111

Herd size: 100+ total herd size 0.018564 0.006389 0.049 2.906 0.00366*

Livestock diversity: own 2+ domestic animal types 0.5345 0.662073 1.535 1.807 0.08194**

Land size: own 100+ acres 0.00847 0.002773 0.018 3.055 0.00225*

HH members employed: 3+ members employed ‐0.18518 0.40269 0.385 ‐0.46 0.64561

Credit access: have no access to credit ‐0.95355 0.251682 2.954 ‐0.962 0.74617

Livestock mobility: able to move livestock freely 0.933049 0.203249 2.903 4.775 0.08380**

Temperature: noticed increase ‐0.84119 1.821335 ‐2417 ‐0.462 0.64418

Rainfall: noticed decrease ‐0.04989 0.67590 0.123 ‐0.432 0.8765

Drought: experience drought within the last 10 years ‐0.34234 0.8650 1.4971 ‐0.32967 0.71607

Floods: experience floods within last the 10 years ‐0.012425 3.215686 0.042 ‐0.004 0.99692

Drought frequency: every year ‐0.291552 0.751606 1.292 ‐0.388 0.69809

Floods frequency: every year ‐1.53658 0.664387 2.154 ‐2.318 0.8171

SE = standard error, OR= odd ratio, z is score of two sample test. The statistical significant of the 299 p value was expressed at 5%*, and 10% ** 300

Several studies (Kakota et al., 2011; Tesso et al., 2012; Opiyo 2014) conducted in pastoral 301 communities in Eastern Africa reported that female headed household are usually not empowered 302 enough to take decisions during extreme climatic events such as drought and are frequently without 303 access to credit services and adequate capital assets. They are also not able to own large herds to 304 manage household’s daily requirements. This shows the need to specifically target pastoralist women 305



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in climate change adaptation planning in arid and semi‐arid lands of Kenya. This study also concurs 306 with Blench (2000) which reported the significant influence of level of education on vulnerability in 307 similar ecosystem. 308

The significant influence of herd size, livestock diversity, access to credit, land size and livestock 309 mobility is also reported in this study. These factors enhance the ability of households to cope during 310 extreme climatic events and reduce their vulnerability to climate change and its extremes. This agrees 311 with studies by Eriksen et al. (2005) and Notenbaert et al. (2013) who also reported some of these 312 factors as key determinant of household vulnerability to climate variability and change in rural 313 communities. The result is also consistent with previous studies by Kakota et al. (2011) and Opiyo 314 (2014) conducted in similar ecosystem. 315

4. Conclusion 316

This study used indicators developed jointly by the researcher and the Maasai communities to 317 analyze household vulnerability of Maasai communities in Kajiado east. Categorization of 318 vulnerability levels using maps is useful for government both at the National and County level for 319 efficient resource allocation to the wards. Human adaptive response to climate change occurs at the 320 local and household level where the climate variability is experienced. It is therefore crucial to 321 understand vulnerability at the household level for timely intervention and also for development of 322 evidence based policies that will lead to effective adaptation programmes for long term resilience. 323

The vulnerability map shows that households in Kitengela ward which is the most developed 324 ward in terms of access to basic amenities is the least vulnerable ward in Kajiado East. Result also 325 shows that indicators such as gender of household head, level of education, access to credit facilities, 326 access to extension services and herd’s mobility significantly affects vulnerability of Maasai 327 pastoralist to climate change and its effect. It is therefore necessary for government at all levels to 328 develop policies and programmes that will address the huge infrastructural deficit in Kajiado county, 329 as this will not only reduce vulnerability to climate extremes, it will also reduce the huge poverty 330 level which currently stands at about 50% (GOK, 2013). 331

The study concludes that there is disparity in the vulnerability levels of households within 332 communities and also among wards in Kajiado east. Resilience intervention should therefore be 333 specific, targeting wards within the Counties and also particular households within the communities. 334 Interventions such as women empowerment, access to extension agents, provision of basic 335 infrastructures such as electricity, water, and good roads, free herd mobility and access to credit 336 facilities will increase resilience of Maasai pastoralist in Kajiado East to the effect of climate change 337 and variability. 338

Acknowledgments: 339

Author Contributions: 340

Conflicts of Interest: 341

References 342

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